Polling Strategy Research

As more and more data gets released about the 2016 election, the decisive factor for Trump occupying the White House remains elusive (was it more Putin or more Comey?). However, we can begin to eliminate some reasons people have given. Several articles in the immediate aftermath placed the blame on Libertarian Gary Johnson and Green Jill Stein (see here, here, here, here, among many others). On the surface, it does look like they harmed Hillary Clinton. Both Johnson and Stein ran in 2012 and received 1.35% of the vote (.99% for Johnson, .36% for Stein). In 2016, they each tripled their support (3.27% for Johnson, 1.06% for Stein). Trump got a smaller share (45.94%) than Mitt Romney (47.15%), but Hillary dropped by even more (48.03% compared to 51.01% for Obama). Thanks to the Cooperative Congressional Election Study (CCES), we can examine the minor party vote with more rigor. No matter how you cut it, Johnson, Stein, McMullin, and the rest did not make a difference. They ended up masking more Trump supporters than ones for Hillary.

Trump gained disproportionate support in the final weeks of October. The CCES interviewed people both before and after the election. Taking those who said they are registered to vote and did surveys before and after election day (44,667 total interviews), Trump gained seven points while Hillary gained just three. Because of the wisdom of several old, rich, white slaveowners 230 years ago, that was enough for Trump to “win”.

Trump kept a greater share of his voters than Hillary did her. More than triple the share of initial Hillary supporters went for Trump than vice versa (2.6% vs. 0.8%). Half of initial Johnson and Stein supporters went for someone else. Johnson voters tilted more towards Trump while Stein voters went for Hillary. Those undecided went for Trump by a 50.0% to 34.2% margin.

Using Random Forest regression, we developed a Trump/Clinton model based on the demographics and responses in the poll. We trained the data using a random selection of 15% of the poll respondents and applied it to the remaining 85% (click here for data and R code). This gives us a probability of voting for Trump and Clinton for each participant. The model works well in classifying voters.

Assigning the candidate with the higher probability as the predicted vote, the model gets 95.8% of Clinton’s voters and 94.2% of Trump’s voters correct (the dots above the diagonal are mostly blue and the ones below red). Among Johnson’s voters, 33.7% have a higher probability of voting for Clinton and 66.3% for Trump (note the higher density of observation below the diagonal line).

Among Stein’s voters, 81.5% have a higher probability of voting for Clinton and 18.5% for Trump. This level of support for Trump among Stein voters should not surprise given the hostility her prominent supporters had for Clinton (see here, here, here) and her ties to prominent Trump surrogates:

Moreover, 12% of Bernie Sanders primary voters went for Trump, as did 11% of Obama 2012 voters and 5% of those who consider themselves ‘liberal’.

Allocating the minor party vote to Hillary and Trump results in Trump expanding his margins in the key swing states of Wisconsin, Michigan, Pennsylvania, and Florida.

Whatever Hillary would gain from Stein voters, she loses far more with the other candidates removed. But what if we just get rid of Stein? If we allocated just Stein voters and not anyone else, Hillary still loses. Even though Stein got more votes than what Trump won by in the decisive states of Wisconsin, Michigan, and Pennsylvania, there is no data that justifies 100% of Stein voters going to Hillary. If Hillary received 81.5% of Stein voters, she only flips Michigan.

Given the attitudes of voters that we can examine more deeply, we can’t justify “blaming” Gary Johnson, Jill Stein, Evan McMullin, and other people either on the ballot or written in for Trump. The Orange Idiot would have received 270+ Electoral Voters in both a two-party race and one without Jill Stein.

At the end of last week, the Senate concluded voting on President Trump’s first round of cabinet nominations. His initial Labor Secretary nominee, Andy Puzder, dropped out because he couldn’t suppress the interview of his ex-wife describing how he beat her up (Trump hires the best people). The Senate haven’t held hearings on his next nominee Alexander Acosta (another prize). The Senate held votes on 18 nominees. Among Republicans, Trump’s nominees received 99.5% of the Yes votes available in the chamber (a few missed votes). Democrats only voted to approve Trump’s choices 37.7% of the time.

Joe Manchin from West Virginia voted for the most nominees. Kirsten Gillibrand from New York approved of the fewest.

Looking beyond the percentages to see how the Democrats voted compared to each other to the correlations between senators, most voted with each other with Manchin as a clear outlier.

Looking at Senators who voted with each other most of the time (correlation coefficient >.8), clear groups emerge:

Tom Udall’s vote correlates to his fellow New Mexican Martin Heinrich, but with no other senator. Heinrich correlates with just Ron Wyden. They are close to Cory Booker, Elizabeth Warren, and Ed Markey who all voted the same.

Looking ahead to the vote to approve Neil Gorsuch to the US Supreme Court, the GOP need 8 Democratic votes to break a filibuster. If they can get the 7 members at the top of the graph above – Heitkamp (ND), Tester (MT), Donnelly (IN), King (ME), Warner (VA), McCaskill (MO), Kaine (VA) – along with Manchin are the most vulnerable to flip. Ironically, it would be Hillary Clinton’s vice presidential nominee who could decide to seat Neil Gorsuch in the chair stolen away from Merrick Garland.

Finally, many senators voted the exact same way on each nominee. The chart above tease them out. There are nine different clusters, with the largest containing six senators. Trump’s nominees divided the Democratic caucus but they allow us to see the different groups emerging. Advocates with limited time and staff should prioritize hitting 1–2 senators in each group to maximize reach.

Only 10 recent polls in New York and the results have been pretty stable. Hillary Clinton has a small but significant lead and comes in at just over 50%. Far from over, but Bernie is going to need to shake up the dynamic in the state or hope the polls are wrong (with a heavy dose of Emerson, Siena, and Quinnipiac, this could be).

Donald Trump is running away with the nomination because he has performed consistently across the country. He gets roughly the same percentage of the vote in deep red counties as he does in deep blue counties. Ted Cruz and John Kasich don’t. Cruz comes closest to Trump in counties where Obama got under 35% of the vote in 2012 and does much worse in counties Obama won.

Conversely, Kasich does more than three times better in counties where Obama got 65%+ than he does in counties Obama got less than 35%. Trump beats him everywhere.

Combined, Kasich and Cruz have done better than Trump both in places Obama won and lost.

Unfortunately for the #NeverTrump crowd, Cruz and Kasich’s voters are so incompatible that if either one dropped, a good share their voters would support Trump.

In many states, a significant relationship exists between Trump’s vote in a county and the percentage of adults over 25 with at least an associates degree. The more educated the county, the worse Trump does. The chart below shows the relationship in states where it exists and that Trump won

The relationship is weaker, or non existent, in states that Trump lost:

In a few states that Trump won, his performance in a county was not impacted by how educated it is.

Finally, the Hillary Clinton and Bernie Sanders have significantly outraised the Republicans in campaign hard money. The Republicans have raised far less money than both successful and unsuccessful Republican campaigns in the past even though individuals can give more money to campaigns than they could in the past.

Nicola is right. Independence supporters still need to do a lot of work to convince the country that they are better off independent. For all the SNP’s success in the last 18 months, they have yet to do this.

A positive correlation (+.855) occurs between the share of Kasich and Rubio support and attaining a degree.

Trump picked the right demographic group to build a durable base on. Ted Cruz staked his on religiosity and ideology. That’s good to win Iowa and a few other states, but when the race moves to the northeast, west coast, or Rust Belt, the deeply religious and conservative simply don’t exist in the same numbers. People without a college degree though, make up significant shares of the electorate everywhere. It’s how Trump can win in Alabama and Massachusetts, especially with multiple candidates dividing up support of college graduates.

Republicans have come to vote in 1,061 counties over 13 states (Minnesota and Alaska have not reported by county). More than half have taken place in deep red counties where Obama got less than 35% in 2012.

2012 Obama Vote

Number of Counties

Share of the Total Vote

35% or less

553

41.3%

35% – 40%

110

9.3%

40% – 45%

98

12.0%

45% – 50%

80

9.9%

50% – 55%

53

7.3%

55% – 60%

65

10.4%

60% – 65%

42

6.0%

65%+

60

3.8%

Trump has won in both strong Obama counties, strong Romney candidates, and everywhere in between. Ted Cruz does best in the deep red counties but still loses to Trump. He does worse where Obama did better.

Rubio does worst in the deep red counties. He does better than Cruz in the counties Obama won but still doesn’t beat Trump.

Kasich also does better in places Obama won.

Only the combination of Kasich and Rubio beats Trump, but only in the strong Obama counties. Trump beats them in the Romney counties. Good thing for Trump, Kasich plans to stay in the race so he and Rubio can divide support and Trump can win with support in the low 30s like he did in Virginia.

Any other candidate with the support Trump has in all parts of the country would be declared the nominee and the party would rally around him. Instead we get Willard Romney attacking him.

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